The consistency regularization is widely used in semi-supervised learning. For example, [1]} designs a teacher-student consistency to take advantage of the dataset without segmentation labels. In detail, the student model is optimized by the consistency loss with regard to the targets predicted by the teacher model. [2]} takes advantage of the consistency loss to boost the detection performance. [3]} put forward cross-task consistency based on inference-path invariance. [2]} introduces the prediction of level set function as additional tasks and construct dual-task consistency.
